Machine-Learning-Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons

Coordination polymers (CPs) with infinite metal-sulfur bond networks have unique electrical conductivities and optical properties. However, the development of new (-M-S-)(n)-structured CPs is hindered by difficulties with their crystallization. Herein, we describe the use of machine learning to optimize the synthesis of trithiocyanuric acid (H(3)ttc)-based semiconductive CPs with infinite Ag-S bond networks, report three CP crystal structures, and reveal that isomer selectivity is mainly determined by proton concentration in the reaction medium. One of the CPs, [Ag(2)Httc](n), features a 3D-extended infinite Ag-S bond network with 1D columns of stacked triazine rings, which, according to first-principle calculations, provide separate paths for holes and electrons. Time-resolved microwave conductivity experiments show that [Ag(2)Httc](n) is highly photoconductive (phi sigma mu(max)=1.6x10(-4) cm(2) V-1 s(-1)). Thus, our method promotes the discovery of novel CPs with selective topologies that are difficult to crystallize.

Automated summary

The study optimized the synthesis of semiconductor coordination polymers based on trithiocyanuric acid using machine learning, and identified a CP with excellent photoconductivity in its structure.